Dispatching Parallel Agents

Protocol Version: 2.0.0
Status: MANDATORY
Last Updated: 2025-01-XX

Overview

When you have multiple unrelated failures (different test files, different subsystems, different bugs), investigating them sequentially wastes time. Each investigation is independent and can happen in parallel.

Core principle: Dispatch one agent per independent problem domain. Let them work concurrently.

MANDATORY REQUIREMENT: You MUST use parallel execution when:

• 3+ independent failures exist
• Tasks can be divided into independent components
• No shared state conflicts
• Different subsystems or file sets are involved

When to Use (MANDATORY)

digraph when_to_use {
    "Multiple failures?" [shape=diamond];
    "Are they independent?" [shape=diamond];
    "Single agent investigates all" [shape=box];
    "One agent per problem domain" [shape=box];
    "Can they work in parallel?" [shape=diamond];
    "Sequential agents" [shape=box];
    "Parallel dispatch [MANDATORY]" [shape=box, style="bold"];

    "Multiple failures?" -> "Are they independent?" [label="yes"];
    "Are they independent?" -> "Single agent investigates all" [label="no - related"];
    "Are they independent?" -> "Can they work in parallel?" [label="yes"];
    "Can they work in parallel?" -> "Parallel dispatch [MANDATORY]" [label="yes"];
    "Can they work in parallel?" -> "Sequential agents" [label="no - shared state"];
}

MANDATORY - You MUST use parallel execution when:

• 3+ test files failing with different root causes
• Multiple subsystems broken independently
• Each problem can be understood without context from others
• No shared state between investigations
• Complex tasks can be divided into independent components
• Different file sets or subsystems are involved

Exception - Sequential execution required when:

• Failures are related (fix one might fix others)
• Need to understand full system state
• Agents would interfere with each other
• Shared state dependencies exist

The Pattern (MANDATORY)

1. Identify Independent Domains (MANDATORY)

MANDATORY ACTION: Group failures or tasks by what's broken or what needs to be done:

• File A tests: Tool approval flow
• File B tests: Batch completion behavior
• File C tests: Abort functionality
• Subsystem X: Network configuration
• Subsystem Y: Visual effects rendering

Each domain is independent - fixing tool approval doesn't affect abort tests.

MANDATORY: You MUST identify all independent domains before proceeding.

2. Divide Complex Tasks into Parallelizable Components (MANDATORY)

MANDATORY ACTION: For complex tasks, you MUST decompose them into parallelizable components:

• Component 1: Independent subsystem A
• Component 2: Independent subsystem B
• Component 3: Independent test file C

Each component must be:

• Independent (no shared state)
• Self-contained (all context provided)
• Clearly scoped (specific goal)

3. Create Focused Agent Tasks (MANDATORY)

MANDATORY ACTION: Each agent gets:

• Specific scope: One test file, subsystem, or component
• Clear goal: Make these tests pass / implement this component
• Constraints: Don't change other code
• Expected output: Summary of what you found and fixed

4. Dispatch in Parallel (MANDATORY)

// In Claude Code / AI environment
Task("Fix agent-tool-abort.test.ts failures")
Task("Fix batch-completion-behavior.test.ts failures")
Task("Fix tool-approval-race-conditions.test.ts failures")
// All three run concurrently

4. Review and Integrate

When agents return:

• Read each summary
• Verify fixes don't conflict
• Run full test suite
• Integrate all changes

Agent Prompt Structure

Good agent prompts are:

1. Focused - One clear problem domain
2. Self-contained - All context needed to understand the problem
3. Specific about output - What should the agent return?

Fix the 3 failing tests in src/agents/agent-tool-abort.test.ts:

1. "should abort tool with partial output capture" - expects 'interrupted at' in message
2. "should handle mixed completed and aborted tools" - fast tool aborted instead of completed
3. "should properly track pendingToolCount" - expects 3 results but gets 0

These are timing/race condition issues. Your task:

1. Read the test file and understand what each test verifies
2. Identify root cause - timing issues or actual bugs?
3. Fix by:
   - Replacing arbitrary timeouts with event-based waiting
   - Fixing bugs in abort implementation if found
   - Adjusting test expectations if testing changed behavior

Do NOT just increase timeouts - find the real issue.

Return: Summary of what you found and what you fixed.

Common Mistakes

❌ Too broad: "Fix all the tests" - agent gets lost ✅ Specific: "Fix agent-tool-abort.test.ts" - focused scope

❌ No context: "Fix the race condition" - agent doesn't know where ✅ Context: Paste the error messages and test names

❌ No constraints: Agent might refactor everything ✅ Constraints: "Do NOT change production code" or "Fix tests only"

❌ Vague output: "Fix it" - you don't know what changed ✅ Specific: "Return summary of root cause and changes"

When NOT to Use

Related failures: Fixing one might fix others - investigate together first Need full context: Understanding requires seeing entire system Exploratory debugging: You don't know what's broken yet Shared state: Agents would interfere (editing same files, using same resources)

Real Example from Session

Scenario: 6 test failures across 3 files after major refactoring

Failures:

• agent-tool-abort.test.ts: 3 failures (timing issues)
• batch-completion-behavior.test.ts: 2 failures (tools not executing)
• tool-approval-race-conditions.test.ts: 1 failure (execution count = 0)

Decision: Independent domains - abort logic separate from batch completion separate from race conditions

Dispatch:

Agent 1 → Fix agent-tool-abort.test.ts
Agent 2 → Fix batch-completion-behavior.test.ts
Agent 3 → Fix tool-approval-race-conditions.test.ts

Results:

• Agent 1: Replaced timeouts with event-based waiting
• Agent 2: Fixed event structure bug (threadId in wrong place)
• Agent 3: Added wait for async tool execution to complete

Integration: All fixes independent, no conflicts, full suite green

Time saved: 3 problems solved in parallel vs sequentially

Key Benefits

1. Parallelization - Multiple investigations happen simultaneously
2. Focus - Each agent has narrow scope, less context to track
3. Independence - Agents don't interfere with each other
4. Speed - 3 problems solved in time of 1

Verification

After agents return:

1. Review each summary - Understand what changed
2. Check for conflicts - Did agents edit same code?
3. Run full suite - Verify all fixes work together
4. Spot check - Agents can make systematic errors

Real-World Impact

From debugging session (2025-10-03):

• 6 failures across 3 files
• 3 agents dispatched in parallel
• All investigations completed concurrently
• All fixes integrated successfully
• Zero conflicts between agent changes